PREDICTION MODEL FOR WHEEL LOADING IN GRINDING USING VIBRATION ANALYSIS AND ANN, 59-66.

K. Viswanathan,∗ A. Krishnakumari,∗∗ and D. Dinakaran∗∗∗

References

  1. [1] E. Susic and I. Grabec, Characterization of the grinding processby acoustic emission, International Journal of Machine Tools& Manufacture, 40, 2000, 225–238.
  2. [2] L. De Chiffre, P. Lonardo, and H. Trumpold, Quantitativecharacterization of surface texture, CIRP Annals, 49(2), 2000,635–642,644–652.
  3. [3] J.S. Kwak and J.S. Bok, Trouble diagnosis of the grinding pro-cess by using acoustic emission signals, International Journalof Machine Tools & Manufacture, 41, 2001, 899–913.
  4. [4] A. Hassui and A.E. Diniz, Correlating surface roughness andvibration on plunge grinding of steel, International Journal ofMachine Tools & Manufacture, 43, 2003, 855–862.
  5. [5] Q. Liu, X. Chen, and N. Gindy, Fuzzy pattern recognition ofAE signals for grinding burn, International Journal of MachineTools & Manufacture, 45, 2005, 811–818.
  6. [6] W.T. Liao, C.F. Ting, J. Qu, and P.J. Blau, Wavelet-basedmethodology for grinding wheel condition monitoring, Inter-national Journal of Machine Tools & Manufacture, 47, 2007,580–592.
  7. [7] S. Malkin and C. Guo, Thermal analysis of grinding, CIRPAnnals, 56(2), 2007, 760–782.
  8. [8] X. Huang and Y. Gao, A discrete system model for form errorcontrol in surface grinding, International Journal of MachineTools & Manufacture, 50, 2010, 219–230.
  9. [9] R. Teti and K. Jemielniak, Advanced monitoring of machiningoperations, CIRP Annals, 59, 2010, 717–739.
  10. [10] V. Gopan and L.D. Wins, Quantitative analysis of grindingwheel loading using image processing, Procedia Technology,25, 2016, 885–891.
  11. [11] J.-Y. Yang, B.-H. Xia, Z. Chen, T.-L. Li, and R. Liu, Vibration-based structural damage identification: A review, Interna-tional Journal of Robotics and Automation, 35(2), 2020. DOI:10.2316/J.2020.206-0259.
  12. [12] I. Tanyer, E. Tatlicioglu, and E. Zergeroglu, Neural net-work based robust control of an aircraft, InternationalJournal of Robotics and Automation, 35(1), 2020, DOI:10.2316/J.2020.206-0074.
  13. [13] P. Kanakarajan, S. Sundaram, A. Kumaravel, R. Rajasekar,and R. Venkatachalam, Prediction of the surface roughnessand wheel wear of modern ceramic material (Al2O3) dur-ing grinding using multiple regression analysis model, IndianJournal of Engineering and Materials Sciences, 24, 2017,182–186.
  14. [14] G. Kant and K.S. Sangwan, Predictive modelling and opti-mization of machining parameters to minimize surface rough-ness using artificial neural network, Procedia CIRP, 31, 2015,453–458.
  15. [15] E. Garc´ıa-Plaza, P.J. N´u˜nez, D.R. Salgado, I. Cambero, J.M.Herrera Olivenza, and J. Garc´ıa Sanz-Calcedo, Surface finishmonitoring in taper turning CNC using artificial neural networkand multiple regression methods, Procedia Engineering, 63,2013, 599–607.
  16. [16] C. Sreeprdha and A. Krishnakumari, Neural network modelfor condition monitoring of wear & film thickness in gear box,Neural Computer & Applications, 24, 2014, 1943–1952.65

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